Abstract: Embodiments of this disclosure relate to systems and methods for determining a set of one or more identified characteristics correlated with high performing projects. Methods include receiving communication data from a plurality of servers, the communication data associated with a plurality of conversations involving one or more users. The communication data is converted into a common format and used to generate a graph, the graph based upon characteristics identified in the communication data and users involved with the plurality of conversations. The communication data can be clustered according to the characteristics and the users, thereby generating one or more clusters around at least one of a characteristic and a user. User data and project data can be generated based on the one or more clusters and be used to determine the set of one or more identified characteristics correlated with high performing projects.

Abstract: Embodiments described herein generate proximal groupings of wireless signals based upon the temporal persistence and spatial proximity of the wireless signals as observed by a plurality of observer devices. For example, a first observer device may observe a first set of wireless signals at a first timepoint and a second observer device may observe a second set of wireless signals at a second timepoint. The first observer device may again observe a third set of wireless signals at a third timepoint. Based upon these observations, a server may generate a proximal grouping a wireless signals containing a subset of the first, second, third of wireless signals based upon temporal persistence and spatial proximity. Temporal persistence may be based upon the repeated observations of the subset of wireless signals across different timepoints and the spatial proximity may be based upon the proximity of locations of the observer devices.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprising an analytic server, which builds a pipeline of five classifiers to validate whether an electronic device is operated by a human user. The server monitors wireless signals detected by different electronic devices by triggering a signal scanning function on the electronic devices. Based on the wireless signals reported by different electronic devices, the server generates a set of signal clusters. The server applies the pipeline of five classifiers on the wireless signals detected by an electronic device by exploiting the signal clusters included in the wireless signals. If any of the classifiers determines the electronic device is a legitimate human device, the server validates the electronic device; otherwise, the server applies the next classifier. If after a number of classifiers, none of the classifiers validates the device usage, the server determines that the device is not operated by a human.

Abstract: Embodiments of this disclosure relate to systems and methods for determining a set of one or more identified characteristics correlated with high performing projects. Methods include receiving communication data from a plurality of servers, the communication data associated with a plurality of conversations involving one or more users. The communication data is converted into a common format and used to generate a graph, the graph based upon characteristics identified in the communication data and users involved with the plurality of conversations. The communication data can be clustered according to the characteristics and the users, thereby generating one or more clusters around at least one of a characteristic and a user. User data and project data can be generated based on the one or more clusters and be used to determine the set of one or more identified characteristics correlated with high performing projects.

Abstract: A system and method for generating a risk score for an observer device based on the wireless signals that are emitted from one or more wireless devices is provided. The system and method includes receiving, by an analytic server, a request for tracing data associated with a first device identifier of a first observer device; generating, by the analytic server responsive to receiving the request, tracing data including a second identifier of a second observer device and a risk score indicative of a degree of association between the first observer device and the second observer device; and transmitting, by the analytic server, the tracing data to a human resource (HR) server associated with an organization, wherein the tracing data causes the HR server to notify the second observer device.

Abstract: Disclosed herein are embodiments of systems, methods, and products that generate semantic resolutions of wireless signals. An analytic server may train a plurality of inductive classifiers generate a respective set of deduction rules. For example, the analytic server may train a first inductive classifier to mine a first set of deduction rules matching service set identifiers (SSIDs) with business entities. As another example, the analytic server may train a second inductive classifier to mine a second set of deduction rules matching proximal groupings of wireless signals (also referred to as hyperclusters) with business entities. Upon receiving an unresolved wireless signal, the analytic server may apply at least one of the first and second set of deduction rules to assign a semantic meaning to the wireless signal.

Abstract: Embodiments described herein generate proximal groupings of wireless signals based upon the temporal persistence and spatial proximity of the wireless signals as observed by a plurality of observer devices. For example, a first observer device may observe a first set of wireless signals at a first timepoint and a second observer device may observe a second set of wireless signals at a second timepoint. The first observer device may again observe a third set of wireless signals at a third timepoint. Based upon these observations, a server may generate a proximal grouping a wireless signals containing a subset of the first, second, third of wireless signals based upon temporal persistence and spatial proximity. Temporal persistence may be based upon the repeated observations of the subset of wireless signals across different timepoints and the spatial proximity may be based upon the proximity of locations of the observer devices.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprising an analytic server, which builds a pipeline of five classifiers to validate whether an electronic device is operated by a human user. The server monitors wireless signals detected by different electronic devices by triggering a signal scanning function on the electronic devices. Based on the wireless signals reported by different electronic devices, the server generates a set of signal clusters. The server applies the pipeline of five classifiers on the wireless signals detected by an electronic device by exploiting the signal clusters included in the wireless signals. If any of the classifiers determines the electronic device is a legitimate human device, the server validates the electronic device; otherwise, the server applies the next classifier. If after a number of classifiers, none of the classifiers validates the device usage, the server determines that the device is not operated by a human.

Abstract: Systems and methods for creating pattern awareness and proximal deduction of wireless devices is disclosed. The methods allow wireless awareness among a plurality of wireless signals and the ability of assigning meaning to a group of wireless signals associated with an observing WED, or wireless signal detector and take action based on the meaning.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which tracks and locates electronic devices (e.g., wireless devices and devices enabled with wireless transmitters). The analytic server monitors wireless signals detected by different observer devices at different time points. The analytic server analyzes the wireless signals to build signal graph and generate hyperclusters. When the analytic server receives a request of finding a lost/missing device, the analytic server receives observations from observer devices that detect the wireless signals from the missing device, determine signal context (e.g., hypercluster) and geolocation of the missing device based on observations from observer devices, performs resolution to narrow down the geolocation. The analytic server also combats different forms of active attacks. The analytic server generates a graphical user interface comprising the location information of the missing device.

Abstract: Hardware and/or software systems, devices, networks, and methods for managing for access authentication, and verification to devices, networks, and systems based on a wireless signal identity developed for wireless-enabled devices based on wireless signal information associated with wireless signals previously detected by the wireless-enabled devices. The system may be used to authenticate access requests, transactions, etc. based on comparison between wireless signal information associated with wireless signals presently detected by the WED and its wireless signal identity via a management platform and/or the WED itself.

Abstract: A distributed system includes two or more components, where at least one of the components is a Policy Decision Point (PDP). The PDP is capable of requesting information from another component of the distributed system, and the PDP is capable of executing an authorization process based on one or more policies defined in a policy language. The policy language includes a communicate command, an execution of which causes the PDP to request information from another component in the distributed system. The policy language also includes a fail operator, which defines handling of failures of the communicate command. An analysis tool for analyzing a result of an authorization process in a Policy Decision Point is also described.